Deep uncertainty in long-term hurricane risk: Scenario generation and implications for future climate experiments

Current projections of long-term trends in Atlantic hurricane activity due to climate change are deeply uncertain, both in magnitude and sign. This creates challenges for adaptation planning in exposed coastal communities. We present a framework to support the interpretation of current long-term tropical cyclone projections, which accommodates the nature of the uncertainty and aims to facilitate robust decision making using the information that is available today. The framework is populated with projections taken from the recent literature to develop a set of scenarios of long-term hurricane hazard. Hazard scenarios are then used to generate risk scenarios for Florida using a coupled climate–catastrophe modeling approach. The scenarios represent a broad range of plausible futures; from wind-related hurricane losses in Florida halving by the end of the century to more than a four-fold increase due to climate change alone. We suggest that it is not possible, based on current evidence, to meaningfully quantify the relative confidence of each scenario. The analyses also suggest that natural variability is likely to be the dominant driver of the level and volatility of wind-related risk over the coming decade; however, under the highest scenario, the superposition of this natural variability and anthropogenic climate change could mean notably increased levels of risk within the decade. Finally, we present a series of analyses to better understand the relative adequacy of the different models that underpin the scenarios and draw conclusions for the design of future climate science and modeling experiments to be most informative for adaptation.     

The Consistency of IPCC's SRES Scenarios to 1990–2000 Trends and Recent Projections

The greenhouse gas emissions scenarios published by the IPCC in the Special Report on Emission Scenarios (SRES) continue to serve as a primary basis for assessing future climate change and possible response strategies. These scenarios were developed between 1996 and 1999 and sufficient time has now passed to make it worth examining their consistency with more recent data and projections. The comparison performed in this paper includes population, GDP, energy use, and emissions of CO₂, non-CO₂ gases and sulfur. We find the SRES scenarios to be largely consistent with historical data for the 1990–2000 period and with recent projections. Exceptions to this general observation include (1) in the long-term, relatively high population growth assumptions; in some regions, particularly in the A2 scenario; (2) in the medium-term, relatively high economic growth assumptions in the LAM (Latin America, Africa and Middle East) region in the A1 scenario; (3) in the short-term, CO₂ emissions projections in A1 that are somewhat higher than the range of current scenarios; and (4) substantially higher sulfur emissions in some scenarios than in historical data and recent projections. In conclusion, given the relatively small inconsistencies for use as global scenarios there seems to be no immediate need for a large-scale IPCC-led update of the SRES scenarios that is solely based on the SRES scenario performance vis-a-vis data for the 1990–2000 period and/or more recent projections. Based on reported findings, individual research teams could make, and in some cases already have made, useful updates of the scenarios.     

Characterizing climate change risks by linking robust decision frameworks and uncertain probabilistic projections

There is increasing concern that avoiding climate change impacts will require proactive adaptation, particularly for infrastructure systems with long lifespans. However, one challenge in adaptation is the uncertainty surrounding climate change projections generated by general circulation models (GCMs). This uncertainty has been addressed in different ways. For example, some researchers use ensembles of GCMs to generate probabilistic climate change projections, but these projections can be highly sensitive to assumptions about model independence and weighting schemes. Because of these issues, others argue that robustness-based approaches to climate adaptation are more appropriate, since they do not rely on a precise probabilistic representation of uncertainty. In this research, we present a new approach for characterizing climate change risks that leverages robust decision frameworks and probabilistic GCM ensembles. The scenario discovery process is used to search across a multi-dimensional space and identify climate scenarios most associated with system failure, and a Bayesian statistical model informed by GCM projections is then developed to estimate the probability of those scenarios. This provides an important advancement in that it can incorporate decision-relevant climate variables beyond mean temperature and precipitation and account for uncertainty in probabilistic estimates in a straightforward way. We also suggest several advancements building on prior approaches to Bayesian modeling of climate change projections to make them more broadly applicable. We demonstrate the methodology using proposed water resources infrastructure in Lake Tana, Ethiopia, where GCM disagreement on changes in future rainfall presents a major challenge for infrastructure planning.     

Climate-related uncertainties in projections of the twenty-first century terrestrial carbon budget: off-line model experiments using IPCC greenhouse-gas scenarios and AOGCM climate projections

A terrestrial ecosystem model (Sim-CYCLE) was driven by multiple climate projections to investigate uncertainties in predicting the interactions between global environmental change and the terrestrial carbon cycle. Sim-CYCLE has a spatial resolution of 0.5°, and mechanistically evaluates photosynthetic and respiratory CO₂ exchange. Six scenarios for atmospheric-CO₂ concentrations in the twenty-first century, proposed by the Intergovernmental Panel on Climate Change, were considered. For each scenario, climate projections by a coupled atmosphere–ocean general circulation model (AOGCM) were used to assess the uncertainty due to socio-economic predictions. Under a single CO₂ scenario, climate projections with seven AOGCMs were used to investigate the uncertainty stemming from uncertainty in the climate simulations. Increases in global photosynthesis and carbon storage differed considerably among scenarios, ranging from 23 to 37% and from 24 to 81 Pg C, respectively. Among the AOGCM projections, increases ranged from 26 to 33% and from 48 to 289 Pg C, respectively. There were regional heterogeneities in both climatic change and carbon budget response, and different carbon-cycle components often responded differently to a given environmental change. Photosynthetic CO₂ fixation was more sensitive to atmospheric CO₂, whereas soil carbon storage was more sensitive to temperature. Consequently, uncertainties in the CO₂ scenarios and climatic projections may create additional uncertainties in projecting atmospheric-CO₂ concentrations and climates through the interactive feedbacks between the atmosphere and the terrestrial ecosystem.     

Scenarios that illuminate vulnerabilities and robust responses

Scenarios exist so that decision makers and those who provide them with information can make statements about the future that claim less confidence than do predictions, projections, and forecasts. Despite their prevalence, fundamental questions remain about how scenarios should best be developed and used. This paper proposes a particular conceptualization of scenarios that aims to address many of the challenges faced when using scenarios to inform contentious policy debates. The concept envisions scenarios as illuminating the vulnerabilities of proposed policies, that is, as concise summaries of the future states of the world in which a proposed policy would fail to meet its goals. Such scenarios emerge from a decision support process that begins with a proposed policy, seeks to understand the conditions under which it would fail, and then uses this information to identify and evaluate potential alternative policies that are robust over a wide range of future conditions. Statistical cluster analyses applied to databases of simulation model results can help identify scenarios as part of this process. Drawing on themes from the decision support literature, this paper first reviews difficulties faced when using scenarios to inform climate-related decisions, describes the proposed approach to address these challenges, illustrates the approach with applications for three different types of users, and concludes with some thoughts on implications for the provision of climate information and for future scenario processes.     

Renewable and low-carbon energies as mitigation options of climate change for China

This article discusses how renewable and low-carbon energies can serve as mitigation options of climate change in China’s power sector. Our study is based on scenarios developed in PowerPlan, a bottom-up model simulating a countries’ power sector and its emissions. We first adjusted the model to China’s present-day economy and power sector. We then developed different scenarios based on story lines for possible future developments in China. We simulated China’s carbon-based electricity production system of today and possible future transitions towards a low-carbon system relying on renewable and low-carbon energies. In our analysis, we compare the business-as-usual scenarios with more sustainable energy scenarios. We found that by increasing the share of renewable and nuclear energies to different levels, between 17% and 57% of all CO₂ emissions from the power sector could be avoided by 2030 compared to the business-as-usual scenario. We also found that electricity generation costs increase when more sustainable power plants are installed. As a conclusion, China has two options: choosing for high climate change mitigation and high costs or choosing for moderate climate change mitigation and moderate costs. In case high climate change mitigation will be chosen, development assistance is likely to be needed to cover the costs.     

Assessing uncertainties in climate change impact analyses on the river flow regimes in the UK. Part 2: future climate

The first part of this paper demonstrated the existence of bias in GCM-derived precipitation series, downscaled using either a statistical technique (here the Statistical Downscaling Model) or dynamical method (here high resolution Regional Climate Model HadRM3) propagating to river flow estimated by a lumped hydrological model. This paper uses the same models and methods for a future time horizon (2080s) and analyses how significant these projected changes are compared to baseline natural variability in four British catchments. The UKCIP02 scenarios, which are widely used in the UK for climate change impact, are also considered. Results show that GCMs are the largest source of uncertainty in future flows. Uncertainties from downscaling techniques and emission scenarios are of similar magnitude, and generally smaller than GCM uncertainty. For catchments where hydrological modelling uncertainty is smaller than GCM variability for baseline flow, this uncertainty can be ignored for future projections, but might be significant otherwise. Predicted changes are not always significant compared to baseline variability, less than 50% of projections suggesting a significant change in monthly flow. Insignificant changes could occur due to climate variability alone and thus cannot be attributed to climate change, but are often ignored in climate change studies and could lead to misleading conclusions. Existing systematic bias in reproducing current climate does impact future projections and must, therefore, be considered when interpreting results. Changes in river flow variability, important for water management planning, can be easily assessed from simple resampling techniques applied to both baseline and future time horizons. Assessing future climate and its potential implication for river flows is a key challenge facing water resource planners. This two-part paper demonstrates that uncertainty due to hydrological and climate modelling must and can be accounted for to provide sound, scientifically-based advice to decision makers.     

Projecting long-term armed conflict risk: An underappreciated field of inquiry?

Little research has been done on projecting long-term conflict risks. Such projections are currently neither included in the development of socioeconomic scenarios or climate change impact assessments nor part of global agenda-setting policy processes. In contrast, in other fields of inquiry, long-term projections and scenario studies are established and relevant for both strategical agenda-setting and applied policies. Although making projections of armed conflict risk in response to climate change is surrounded by uncertainty, there are good reasons to further develop such scenario-based projections. In this perspective article we discuss why quantifying implications of climate change for future armed conflict risk is inherently uncertain, but necessary for shaping sustainable future policy agendas. We argue that both quantitative and qualitative projections can have a purpose in future climate change impact assessments and put out the challenges this poses for future research.     

Vegetation patterns in South America associated with rising CO2: uncertainties related to sea surface temperatures

Spatially precise forecasts of the impacts of climate change on the distribution of major vegetation types are essential for the implementation of effective conservation and land use policy. However, existing studies frequently omit major sources of climate variability that can significantly increase the uncertainty of projections. In this study we demonstrate how different predictions for sea surface temperature (SST) for the first half of the twenty-first century increase the uncertainty associated with forecasts of the future distribution of major ecosystems in South America. This is demonstrated through a numerical experiment using a coupled climate–vegetation model (CCM3-IBIS) for IPCC emission scenario A2 that incorporates the SST data from ten different models. The study reveals an increasing uncertainty in the ability to forecast future vegetation patterns, such that by 2050 the simulation is unable to robustly forecast the vegetation cover in an area equivalent to 28 % in South America (5?×?10⁶ km²). The future of the central and northeastern regions of Brazil is especially uncertain, with outcomes, ranging from savanna, and open shrubland to grassland. Recognizing and managing such uncertainty should be a priority for decision makers.     

Climate science in climate security scenarios

The climate is changing and this will have consequences across the globe, affecting all areas of the natural and human environment in complex ways. One tool for exploring the complex relationship between climate change and security is to use a scenariobased approach as means to develop plausible rather than probable narratives. This approach can explore the range of uncertainty, and help policymakers to visualise the potential consequences of climate change. Scenario methodology has been most comprehensively developed for use in business planning and there are some differences in the way scenarios work for climate security. The most notable is the fact that there is a physical modelling basis for climate projections. This means that the uncertainty range associated with at least this one aspect of the scenario can be systematically sampled. This paper reviews how scenarios have been used in the climate security literature to date, in particular in the grey literature. The integration of climate projections is explored in detail, as the main distinguishing feature of climate security scenarios over other scenario types. Few climate security scenarios have been developed to date, but all those included in this review, regardless of the differences in national conditions, regional climate change and potential responses, came to very similar conclusions. This was despite differences in the changes in climate and non-climate drivers, or differences in the scenario approach and audience. A more systematic and scientific approach to developing the scenario drivers and narratives is recommended, and successfully embedding changes in climate within the socio-economic context, through better integration of inter-disciplinary expertise, is critical.     

Probabilistic climate change projections using neural networks

Anticipated future warming of the climate system increases the need for accurate climate projections. A central problem are the large uncertainties associated with these model projections, and that uncertainty estimates are often based on expert judgment rather than objective quantitative methods. Further, important climate model parameters are still given as poorly constrained ranges that are partly inconsistent with the observed warming during the industrial period. Here we present a neural network based climate model substitute that increases the efficiency of large climate model ensembles by at least an order of magnitude. Using the observed surface warming over the industrial period and estimates of global ocean heat uptake as constraints for the ensemble, this method estimates ranges for climate sensitivity and radiative forcing that are consistent with observations. In particular, negative values for the uncertain indirect aerosol forcing exceeding –1.2 Wm^–2 can be excluded with high confidence. A parameterization to account for the uncertainty in the future carbon cycle is introduced, derived separately from a carbon cycle model. This allows us to quantify the effect of the feedback between oceanic and terrestrial carbon uptake and global warming on global temperature projections. Finally, probability density functions for the surface warming until year 2100 for two illustrative emission scenarios are calculated, taking into account uncertainties in the carbon cycle, radiative forcing, climate sensitivity, model parameters and the observed temperature records. We find that warming exceeds the surface warming range projected by IPCC for almost half of the ensemble members. Projection uncertainties are only consistent with IPCC if a model-derived upper limit of about 5 K is assumed for climate sensitivity.     

The Shared Socioeconomic Pathways and their energy,land use,and greenhouse gas emissions implications: An overview

This paper presents the overview of the Shared Socioeconomic Pathways (SSPs) and their energy, land use, and emissions implications. The SSPs are part of a new scenario framework, established by the climate change research community in order to facilitate the integrated analysis of future climate impacts, vulnerabilities, adaptation, and mitigation. The pathways were developed over the last years as a joint community effort and describe plausible major global developments that together would lead in the future to different challenges for mitigation and adaptation to climate change. The SSPs are based on five narratives describing alternative socio-economic developments, including sustainable development, regional rivalry, inequality, fossil-fueled development, and middle-of-the-road development. The long-term demographic and economic projections of the SSPs depict a wide uncertainty range consistent with the scenario literature. A multi-model approach was used for the elaboration of the energy, land-use and the emissions trajectories of SSP-based scenarios. The baseline scenarios lead to global energy consumption of 400–1200 EJ in 2100, and feature vastly different land-use dynamics, ranging from a possible reduction in cropland area up to a massive expansion by more than 700 million hectares by 2100. The associated annual CO₂ emissions of the baseline scenarios range from about 25 GtCO₂ to more than 120 GtCO₂ per year by 2100. With respect to mitigation, we find that associated costs strongly depend on three factors: (1) the policy assumptions, (2) the socio-economic narrative, and (3) the stringency of the target. The carbon price for reaching the target of 2.6 W/m² that is consistent with a temperature change limit of 2 °C, differs in our analysis thus by about a factor of three across the SSP marker scenarios. Moreover, many models could not reach this target from the SSPs with high mitigation challenges. While the SSPs were designed to represent different mitigation and adaptation challenges, the resulting narratives and quantifications span a wide range of different futures broadly representative of the current literature. This allows their subsequent use and development in new assessments and research projects. Critical next steps for the community scenario process will, among others, involve regional and sectoral extensions, further elaboration of the adaptation and impacts dimension, as well as employing the SSP scenarios with the new generation of earth system models as part of the 6th climate model intercomparison project (CMIP6).     

Variances in the projections,resulting from CLIMEX,Boosted Regression Trees and Random Forests techniques

The aim of this study was to have a comparative investigation and evaluation of the capabilities of correlative and mechanistic modeling processes, applied to the projection of future distributions of date palm in novel environments and to establish a method of minimizing uncertainty in the projections of differing techniques. The location of this study on a global scale is in Middle Eastern Countries. We compared the mechanistic model CLIMEX (CL) with the correlative models MaxEnt (MX), Boosted Regression Trees (BRT), and Random Forests (RF) to project current and future distributions of date palm (Phoenix dactylifera L.). The Global Climate Model (GCM), the CSIRO-Mk3.0 (CS) using the A2 emissions scenario, was selected for making projections. Both indigenous and alien distribution data of the species were utilized in the modeling process. The common areas predicted by MX, BRT, RF, and CL from the CS GCM were extracted and compared to ascertain projection uncertainty levels of each individual technique. The common areas identified by all four modeling techniques were used to produce a map indicating suitable and unsuitable areas for date palm cultivation for Middle Eastern countries, for the present and the year 2100. The four different modeling approaches predict fairly different distributions. Projections from CL were more conservative than from MX. The BRT and RF were the most conservative methods in terms of projections for the current time. The combination of the final CL and MX projections for the present and 2100 provide higher certainty concerning those areas that will become highly suitable for future date palm cultivation. According to the four models, cold, hot, and wet stress, with differences on a regional basis, appears to be the major restrictions on future date palm distribution. The results demonstrate variances in the projections, resulting from different techniques. The assessment and interpretation of model projections requires reservations, especially in correlative models such as MX, BRT, and RF. Intersections between different techniques may decrease uncertainty in future distribution projections. However, readers should not miss the fact that the uncertainties are mostly because the future GHG emission scenarios are unknowable with sufficient precision. Suggestions towards methodology and processing for improving projections are included.     

Spatial Analysis of Environmental Change Impacts on Wheat Production in Mid-Lower North, South Australia

Three environmental change scenarios (the best scenario, the most likely scenario and the worst scenario) were used by the APSIM (Agricultural Production System sIMulator) Wheat module to study the possible impacts of future environmental change (climate change plus pCO₂ change) on wheat production in the Mid-Lower North of South Australia. GIS software was used to manage spatial-climate data and spatial-soil data and to present the results. Study results show that grain yield (kg ha⁻¹) was adversely affected under the worst environmental change scenario (−100% ∼ −42%) and the most likely environmental change scenario (−58% ∼ −3%). Grain nitrogen content (% N) either increased or decreased depending on the environmental change scenarios used and climate divisions (−25% ∼ +42%). Spatial variability was found for projected impact outcomes within climate divisions indicating the necessity of including the spatial distribution of soil properties in impact assessment.     

A new analytic method for finding policy-relevant scenarios

Scenarios play a prominent role in policy debates over climate change, but questions continue about how best to use them. We describe a new analytic method, based on robust decision making, for suggesting narrative scenarios that emerge naturally from a decision analytic framework. We identify key scenarios as those most important to the choices facing decision makers and find such cases with statistical analysis of datasets created by multiple runs of computer simulation models. The resulting scenarios can communicate quantitative judgments about uncertainty as well as support a well-defined decision process without many drawbacks of current approaches. We describe an application to long-term water planning in California.     

Simulating the impacts of climate change on cotton production in India

General circulation models (GCMs) project increases in the earth’s surface air temperatures and other climate changes by the mid or late 21st century, and therefore crops such as cotton (Gossypium spp L.) will be grown in a much different environment than today. To understand the implications of climate change on cotton production in India, cotton production to the different scenarios (A2, B2 and A1B) of future climate was simulated using the simulation model Infocrop-cotton. The GCM projections showed a nearly 3.95, 3.20 and 1.85 °C rise in mean temperature of cotton growing regions of India for the A2, B2 and A1B scenarios, respectively. Simulation results using the Infocrop-cotton model indicated that seed cotton yield declined by 477 kg?ha^?1 for the A2 scenario and by 268 kg?ha^?1 for the B2 scenario; while it was non-significant for the A1B scenario. However, it became non-significant under elevated [CO₂] levels across all the scenarios. The yield decline was higher in the northern zone over the southern zone. The impact of climate change on rainfed cotton which covers more than 60 % of the country’s total cotton production area (mostly in the central zone) and is dependent on the monsoons is likely to be minimum, possibly on account of marginal increase in rainfall levels. Results of this assessment suggest that productivity in northern India may marginally decline; while in central and southern India, productivity may either remain the same or increase. At the national level, therefore, cotton production is unlikely to change with climate change. Adaptive measures such as changes in planting time and more responsive cultivars may further boost cotton production in India.     

The role of land surface processes in regional climate change: a case study of future land cover change over south western Australia

Summary Using a high resolution regional climate model we perform multiple January simulations of the impact of land cover change over western Australia. We focus on the potential of reforestation to ameliorate the projected warming over western Australia under two emission scenarios (A2, B2) for 2050 and 2100. Our simulations include the structural and physiological responses of the biosphere to changes in climate and changes in carbon dioxide. We find that reforestation has the potential to reduce the warming caused by the enhanced greenhouse effect by as much as 30% under the A2 and B2 scenarios by 2050 but the cooling effect declines to 10% by 2100 as CO₂-induced warming intensifies. The cooling effect of reforestation over western Australia is caused primarily by the increase in leaf area index that leads to a corresponding increase in the latent heat flux. This cooling effect is localized and there were no simulated changes in temperature over regions remote from land cover change. We also show that the more extreme emission scenario (A2) appears to lead to a more intense response in photosynthesis by 2100. Overall, our results are not encouraging in terms of the potential to offset future warming by large scale reforestation. However, at regional scales the impact of land cover change is reasonably large relative to the impact of increasing carbon dioxide (up to 2050) suggesting that future projections of the Australian climate would benefit from the inclusion of projections of future land cover change. We suggest that this would add realism and regional detail to future projections and perhaps aid detection and attribution studies.     

Terrestrial biosphere carbon storage under alternative climate projections

This study investigates commonalities and differences in projected land biosphere carbon storage among climate change projections derived from one emission scenario by five different general circulation models (GCMs). Carbon storage is studied using a global biogeochemical process model of vegetation and soil that includes dynamic treatment of changes in vegetation composition, a recently enhanced version of the Lund-Potsdam-Jena Dynamic Global Vegetation Model (LPJ-DGVM). Uncertainty in future terrestrial carbon storage due to differences in the climate projections is large. Changes by the end of the century range from −106 to +201 PgC, thus, even the sign of the response whether source or sink, is uncertain. Three out of five climate projections produce a land carbon source by the year 2100, one is approximately neutral and one a sink. A regional breakdown shows some robust qualitative features. Large areas of the boreal forest are shown as a future CO₂ source, while a sink appears in the arctic. The sign of the response in tropical and sub-tropical ecosystems differs among models, due to the large variations in simulated precipitation patterns. The largest uncertainty is in the response of tropical rainforests of South America and Central Africa.     

Spatial impact of projected changes in rainfall and temperature on wheat yields in Australia

Climate projections over the next two to four decades indicate that most of Australia’s wheat-belt is likely to become warmer and drier. Here we used a shire scale, dynamic stress-index model that accounts for the impacts of rainfall and temperature on wheat yield, and a range of climate change projections from global circulation models to spatially estimate yield changes assuming no adaptation and no CO₂ fertilisation effects. We modelled five scenarios, a baseline climate (climatology, 1901–2007), and two emission scenarios (“low” and “high” CO₂) for two time horizons, namely 2020 and 2050. The potential benefits from CO₂ fertilisation were analysed separately using a point level functional simulation model. Irrespective of the emissions scenario, the 2020 projection showed negligible changes in the modelled yield relative to baseline climate, both using the shire or functional point scale models. For the 2050-high emissions scenario, changes in modelled yield relative to the baseline ranged from ?5 % to +6 % across most of Western Australia, parts of Victoria and southern New South Wales, and from ?5 to ?30 % in northern NSW, Queensland and the drier environments of Victoria, South Australia and in-land Western Australia. Taking into account CO₂ fertilisation effects across a North–south transect through eastern Australia cancelled most of the yield reductions associated with increased temperatures and reduced rainfall by 2020, and attenuated the expected yield reductions by 2050.     

Stabilizing greenhouse gas concentrations at low levels: an assessment of reduction strategies and costs

On the basis of the IPCC B2, A1b and B1 baseline scenarios, mitigation scenarios were developed that stabilize greenhouse gas concentrations at 650, 550 and 450 and – subject to specific assumptions – 400 ppm CO₂-eq. The analysis takes into account a large number of reduction options, such as reductions of non-CO₂ gases, carbon plantations and measures in the energy system. The study shows stabilization as low as 450 ppm CO₂-eq. to be technically feasible, even given relatively high baseline scenarios. To achieve these lower concentration levels, global emissions need to peak within the first two decades. The net present value of abatement costs for the B2 baseline scenario (a medium scenario) increases from 0.2% of cumulative GDP to 1.1% as the shift is made from 650 to 450 ppm. On the other hand, the probability of meeting a two-degree target increases from 0%–10% to 20%–70%. The mitigation scenarios lead to lower emissions of regional air pollutants but also to increased land use. The uncertainty in the cost estimates is at least in the order of 50%, with the most important uncertainties including land-use emissions, the potential for bio-energy and the contribution of energy efficiency. Furthermore, creating the right socio-economic and political conditions for mitigation is more important than any of the technical constraints.